


"""simple docstring"""



from __future__ import annotations
def      UpperCAmelCase     (       _lowercase :   int | str	)						-> bool:


	"""simple docstring"""




	lowerCAmelCase_      							=			str(_lowercase	)
	return n == n[::-1]
def      UpperCAmelCase     (       _lowercase :   int = 1_0_0_0_0_0_0	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			0

	for i in range(1			,   _lowercase	):
		if is_palindrome(_lowercase	) and is_palindrome(bin(_lowercase	).split('''b'''	)[1]	):
			total += i
	return total


if __name__ == "__main__":
		print(solution(int(str(input().strip()))))



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							=							{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['MobileBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileBertForMaskedLM',
		    'MobileBertForMultipleChoice',
		    'MobileBertForNextSentencePrediction',
		    'MobileBertForPreTraining',
		    'MobileBertForQuestionAnswering',
		    'MobileBertForSequenceClassification',
		    'MobileBertForTokenClassification',
		    'MobileBertLayer',
		    'MobileBertModel',
		    'MobileBertPreTrainedModel',
		    'load_tf_weights_in_mobilebert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileBertForMaskedLM',
		    'TFMobileBertForMultipleChoice',
		    'TFMobileBertForNextSentencePrediction',
		    'TFMobileBertForPreTraining',
		    'TFMobileBertForQuestionAnswering',
		    'TFMobileBertForSequenceClassification',
		    'TFMobileBertForTokenClassification',
		    'TFMobileBertMainLayer',
		    'TFMobileBertModel',
		    'TFMobileBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mobilebert import (
		    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileBertConfig,
		    MobileBertOnnxConfig,
		)
		from .tokenization_mobilebert import MobileBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mobilebert_fast import MobileBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilebert import (
				    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertLayer,
				    MobileBertModel,
				    MobileBertPreTrainedModel,
				    load_tf_weights_in_mobilebert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilebert import (
				    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertMainLayer,
				    TFMobileBertModel,
				    TFMobileBertPreTrainedModel,
				)

else:
		import sys

		lowercase_							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)